System, methods, and other embodiments described herein relate to improving right-of-way determinations at an intersection. In one embodiment, a method includes acquire, in a lead vehicle that is a cloud leader of a micro-cloud, observations about the intersection for a set of vehicles including at least one remote vehicle. The set of vehicles are approaching the intersection. The remote vehicle and the lead vehicle are members of the micro-cloud. The method includes deriving an assignment of right-of-ways indicating an order about how the set of vehicles may proceed through the intersection. The method includes providing the assignment to at least the remote vehicle to control right-of-way at the intersection.

The described techniques relate to modifying a trajectory of a vehicle, such as an autonomous vehicle, based on an overlap area associated with an object in the environment. In examples, map data may be used, in part, to generate an initial trajectory for an autonomous vehicle to follow through an environment. In some cases, a yield trajectory may be generated based on detection of the object, and the autonomous vehicle may evaluate a cost function to determine whether to execute the yield or follow the initial trajectory. In a similar manner, the autonomous vehicle may determine a merge location of two lanes of a junction, and use the merge location to update extents of an overlap area to prevent the autonomous vehicle from blocking the junction and/or provide sufficient space to yield to the oncoming vehicle while merging.

An autonomous vehicle (AV) includes a vehicle computing system including one or more processors programmed to receive map data associated with a map of a geographic location, including, one or more local routes in the one or more roadways between the current location of the AV and one or more exit locations, receive sensor data associated with an object detected in an environment surrounding the AV, select a local route of the one or more local routes based on the sensor data and control travel of the AV based on a selected local route of the one or more local routes. The map includes one or more roadways in the geographic location. The one or more exit locations are located between the current location of the AV and the destination location of the AV in a global route in the one or more roadways.

Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. In one instance, a default number of trajectories to be generated may be identified. A set of maneuvering options may be selected from a set of predetermined maneuvering options based on the number of trajectories. The set of maneuvering options may be filtered based on the default number of trajectories. A set of trajectories may be generated based on the filtered set of maneuvering option such that each trajectory of the set corresponds to a different maneuvering behavior. A cost for each trajectory of the set of trajectories may be determined, and one of the trajectories of the set of trajectories may be selected based on the determined costs. The vehicle may be maneuvered in the autonomous driving mode according to the selected one of the trajectories.

A vehicular control system includes a camera that captures image data. The system includes an electronic control unit (ECU) for processing image data captured by the camera. The ECU, via processing by an image processor of image data captured by the camera, determines lane information of a traffic lane along a road being traveled by the equipped vehicle. The ECU determines a lane quality value that represents a confidence in the determined lane information. When the lane quality value exceeds a threshold value, and based at least in part on the determined lane information, the ECU provides a steering command to a steering system of the equipped vehicle to adjust a heading of the equipped vehicle to center the equipped vehicle within the traffic lane of the road being traveled by the equipped vehicle.

Systems and methods for providing navigation to a vehicle may include receiving observation data from one or more sensors of the vehicle, generating projection data corresponding to the one or more traffic participants based on the observation data for each time step within a time period, and predicting interactions between the vehicle, the one or more traffic participants, and the one or more obstacles, based on the projection data of the one or more traffic participants. The systems and methods may further include determining a set of actions by the vehicle corresponding to a probability of the vehicle safely arriving at a target location based on the predicted interactions, and selecting one or more actions from the set of actions and provide the one or more actions to a navigation system of the vehicle, wherein the navigation system uses the navigation data to provide navigation instructions to the vehicle.

Techniques are provided which may be implemented using various methods and/or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external V2X input to determine, send, receive and utilize V2X information and control data, sent between the vehicle and a road side unit (RSU) to determine intersection access and vehicle behavior when approaching the intersection.

Autonomous driving is possible even in a no-lane section. Probe information to be transmitted from a plurality of vehicles is stored. Vehicle travel trajectory information in a no-lane section is extracted from the stored probe information, the vehicle travel trajectory information is extracted per combination of an entry point and an exit point for the no-lane section, the extracted vehicle travel trajectory information per combination of the entry point and exit point for the no-lane section is sorted into a plurality of categories according to a predetermined criterion, and statistical trajectory information is calculated by statistical processing of the travel trajectory information for each of the plurality of categories. The calculated statistical trajectory information is transmitted to the plurality of vehicles as vehicle route information in the no-lane section.

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.

A method for risk-aware game-theoretic trajectory planning is described. The method includes modeling an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment. The method also includes ranking upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories. The method further includes selecting a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.